[soneil]

It's an odd one for me, because he sounds like he's trying to argue against the "when all you have is a hammer" approach to ML - but then goes on to describe SQL as his hammer.

For things like figuring out who your biggest customers are, SQL probably is the right tool for the job. Whale-spotting probably gives a decent bang per buck, and isn't particularly complex.

But when he gets onto recommendations, it starts to look like it's the author who's attached to the wrong tool for the job. His example of recommending sunglasses to people who buy sunglasses is terribly blunt. If someone in my locale, who doesn't regularly buy sunglasses, buys sunglasses; they're probably going on vacation - there's not much sun at home for them. Surely there's a whole raft of things someone excited for their summer holidays would impulse-buy, but the sunglasses they just bought are no longer on the list.

If ML can match them up with with a "going on summer holidays" demographic, and BI wants to sell them the only thing we know they no longer need, it's no longer making a strong case for blunt instruments.